2018/11/13 by Omer Lev, Lev, Omer, Reshef Meir +5
Computer Science · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #cs.GT
paper · pdf · doi:10.48550/arxiv.1811.05529
This is the full version of paper #6080 accepted to AAAI'19
arxiv created 2018/11/13 · arxiv updated 2018/11/15
Decision making under uncertainty is a key component of many AI settings, and in particular of voting scenarios where strategic agents are trying to reach a joint decision. The common approach to handle uncertainty is by maximizing expected utility, which requires a cardinal utility function as well as detailed probabilistic information. However, often such probabilities are not easy to estimate or apply. To this end, we present a framework that allows "shades of gray" of likelihood without probabilities. Specifically, we create a hierarchy of sets of world states based on a prospective poll, with inner sets contain more likely outcomes. This hierarchy of likelihoods allows us to define what we term ordinally-dominated strategies. We use this approach to justify various known voting heuristics as bounded-rational strategies.